Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A system, comprising: a processor configured to: receive structured energy data associated with one or more assets in an automation system, wherein the structured energy data comprises a logical grouping of assets in the automation system, wherein the logical grouping of assets is organized with respect to different manufacturing processes of the automation system; determine energy properties of the one or more assets with respect to the logical grouping over a period of time, wherein the energy properties comprise power usage data of the one or more assets; identify one or more peak demand periods of one or more portions of the one or more assets with respect to the logical grouping during the period of time based on the power usage data of the one or more assets with respect to the logical grouping; identify an energy bottleneck of the one or more portions of the one or more assets with respect to the logical grouping based on the one or more peak demand periods; determine an asset schedule configured to coordinate one or more operations of the portions of the one or more assets according to the logical grouping, wherein the operations are coordinated to reduce an energy demand of the automation system during the one or more peak demand periods to reduce the energy bottleneck; send one or more commands to at least some of the one or more assets to reduce energy consumption according to the asset schedule; and control the one or more assets according to the one or more commands.
This system manages energy consumption in an industrial automation environment. A processor receives structured energy data representing assets logically grouped by manufacturing processes. It determines each asset's power usage, identifies peak demand periods, and pinpoints energy bottlenecks. Based on this, the system creates a schedule to coordinate asset operations, reducing energy demand during peak times to alleviate bottlenecks. Finally, it sends commands to assets, controlling them to reduce energy consumption according to the optimized schedule. The goal is to intelligently control assets to minimize energy waste and address energy bottlenecks within an industrial setting.
2. The system of claim 1 , wherein the commands are configured to shed loads of the assets with respect to the logical grouping, exchange energy between the one or more assets, stagger loads, release stored energy, provide energy back to a grid, or any combination thereof.
Building upon the energy management system, the commands sent to assets to control energy consumption include several options: shedding loads (temporarily reducing power to certain components), exchanging energy between assets, staggering loads to avoid simultaneous peaks, releasing stored energy (like from a battery), providing excess energy back to the power grid, or a combination of these. The system chooses the most appropriate commands, based on the asset grouping and current energy needs, to optimize the overall energy consumption and reduce the energy bottleneck. The logical grouping assists in identifying appropriate resources for each command.
3. The system of claim 1 , wherein the commands are configured to use the different manufacturing processes as a battery.
Expanding on the energy management system, this system treats different manufacturing processes as a kind of battery. The commands sent to assets are designed to leverage these processes. For instance, the system might temporarily increase production in a process that consumes less energy during peak demand, effectively "storing" energy in the form of product inventory. Conversely, it could reduce production in energy-intensive processes. This approach utilizes the flexibility of manufacturing processes to manage energy demand, seeing the processes themselves as a form of energy storage.
4. The system of claim 1 , wherein the structured energy data is generated based on an organizational model data comprising the logical grouping of the assets with respect to one or more functionalities of the automation system.
In the energy management system, the structured energy data, which is used to determine energy properties and identify bottlenecks, is based on an organizational model. This model logically groups assets according to their functionalities within the automation system. The structure of this data reflects how assets work together, so it can be with respect to a functional organization. This organizational grouping of assets forms the basis for creating the structured energy data that supports efficient energy management within the system.
5. A system, comprising: a processor configured to: receive structured energy data associated with one or more assets in an automation system, wherein the structured energy data comprises a logical grouping of assets in the automation system, wherein the logical grouping of assets is organized with respect to different manufacturing processes of the automation system; receive an asset schedule associated with one or more operations of the one or more assets over time; predict an amount of energy of each asset of the one or more assets consumes or produces over the time based on the asset schedule; identify an energy bottleneck of the one or more assets based on the predicted amount of energy each asset consumes or produces; modify the asset schedule based on the predicted amount of energy each asset consumes or produces, wherein the modified asset schedule is configured to maintain an energy target for the automation system and reduce the energy bottleneck of the one or more assets; send one or more commands to at least some of the one or more assets to control the at least some of the one or more assets according to the modified asset schedule; and control the one or more assets according to the one or more commands.
This system optimizes energy usage in an industrial automation environment. A processor receives structured energy data associated with one or more assets, grouped logically by manufacturing processes. It also receives an asset schedule defining operations over time. The system then predicts the energy consumption or production of each asset based on this schedule. After that, the system identifies energy bottlenecks based on these predictions. It modifies the asset schedule to meet a target energy level while reducing the bottleneck. Finally, the system sends commands to control the assets according to the modified schedule.
6. The system of claim 5 , wherein the modified asset schedule is configured to coordinate one or more operations of the one or more assets according to the logical grouping, wherein the operations are coordinated with respect to the logical grouping such that energy associated with the automation system is similar to the energy target.
In the energy-optimizing system, the modified asset schedule coordinates asset operations according to the logical grouping of assets. The system coordinates the operations, considering these logical groupings, to ensure that the automation system's energy consumption closely matches the desired energy target. This allows the system to optimize operations with an awareness of which assets must work together and in what manner, maintaining efficient energy usage in each manufacturing process.
7. The system of claim 5 , wherein the processor is configured to determine confidence values associated with the predicted amount of energy of each asset of the one or more assets consumes or produces, wherein the confidence values correspond to determined accuracies associated with the amount of energy of each asset of the one or more assets consumes or produces, and wherein the processor is configured to modify the asset schedule based at least in part on the confidence values.
Enhancing the energy optimization system, the processor calculates confidence values for each asset's predicted energy consumption or production. These confidence values represent the accuracy of the energy predictions. The system then modifies the asset schedule not only based on the predicted energy but also on these confidence values. Therefore, it favors modifications that rely on more accurate predictions, reducing risk when controlling the assets. The use of confidence values makes the optimization more stable.
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December 12, 2017
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